Movable Antenna (MA) systems enhance channel capacity by leveraging antenna mobility within a continuous spatial region. However, practical performance depends on accurate Channel State Information (CSI) and effective position optimization. This paper proposes a two-stage framework to optimize MA systems under imperfect CSI. First, we design a pilot position optimization method to improve channel estimation accuracy using Compressed Sensing (CS). The estimated channel then provides the necessary CSI for a joint antenna position and precoding optimization scheme. By employing an analytic gradient-based Coordinate Descent approach with binary-search-driven distance constraint handling, we optimize all antenna positions simultaneously to avoid local optima. Furthermore, the Weighted Minimum Mean Square Error (WMMSE) algorithm is integrated to refine the precoders. Simulation results demonstrate that the proposed scheme achieves an approximate 15% performance gain over baseline methods in low-overhead regimes.

To address the issues of slow convergence speed and poor energy efficiency of existing intelligent jamming decision-making in communication countermeasures scenarios, a hierarchical Rainbow deep Q-network (HRDQN) algorithm was proposed. Firstly, a communication system model subject to non-cooperative intelligent jamming was formulated, and the jamming decision-making process was modeled as a Markov decision process (MDP), deriving the suppression coefficient threshold to quantify jamming effectiveness. Secondly, the action space and decision-making method of the agent were designed to improve decision-making efficiency based on a hierarchical structure. Finally, the reward function was designed to combine the suppression coefficient threshold with the estimated jamming-to-signal ratio (JSR) to guarantee stable convergence of the algorithm. Simulation results demonstrate that the proposed algorithm rapidly generates ideal jamming decisions while reducing power consumption, and outperforms traditional algorithms in convergence speed, thereby corroborating the merits of the proposed algorithm.

In this paper, we study simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted secure ground-air communications. Ground nodes upload data to an unmanned aerial vehicle (UAV) with the assistance of a STAR-RIS, while facing eavesdropping threats targeting confidential information from a secure node. Planning UAV trajectories in a full space instead of only on the single side of a STAR-RIS is helpful to make the most of UAV flexibility. However, the transmission rates of ground nodes is hard to express because cascaded channels vary with the UAV's location relative to the STAR-RIS. To address this issue, we propose a unified expression of the rate incorporating the UAV-location-dependent cascaded channels. Based on the proposed expression, we maximize the average secrecy rate (ASR) of a secure node while ensuring the rate requirements of a regular node via robust optimization of STAR-RIS coefficients and UAV trajectory. Specifically, we consider the imperfect eavesdropping channel state information. Due to the non-convex structure, the original mixed integer nonlinear programming problem is decomposed into two subproblems. Semidefinite relaxation, $\mathcal {S}$ -Procedure, and successive convex approximation techniques are used to tackle the non-convex objective function and constraints. Finally, the two subproblems are solved iteratively. Numerical simulations reveal that the proposed scheme achieves the full-space flight of the UAV and outperforms the benchmark schemes in terms of ASR.

Communication security is of paramount importance, where covert communication serves as a vital means to ensure it. This paper investigates the security performance optimization of a covert communication system assisted by cooperative jamming and a reconfigurable intelligent surface (RIS), aiming to enhance the covert transmission rate. Specifically, we analytically derive the warden’s expected detection error probability(DEP) and identify the worst-case warden location that minimizes detection error. Building on this worst-case analysis, we formulate a joint optimization problem to maximize the covert transmission rate by coordinating the transmit power, jamming power, and RIS phase shifts. Numerical results quantify that the warden’s location uncertainty leads to a covert rate degradation of approximately 10%, which can be effectively mitigated by the proposed robust optimization strategy.

Reconfigurable intelligent surface (RIS) is emerging as a promising technique for strengthening the security transmission of the wireless networks. This work investigates the covert communication and the physical layer security of the RIS assisted rate splitting multiple access (RSMA) system at the presence of multiple randomly distributed malicious users, where the non-colluding and the colluding behaviors of malicious users are considered. To characterize the channel statistics, we derive a Gamma distribution to approximate the Nakagami-$m$ channel for RIS assisted links. For the covert communication performance analysis, we first derive the closed form expressions of the detection error probability (DEP) for the non-colluding and the colluding wardens. Then the analytical ergodic covert rate (ECR) is also deduced. For the physical layer security performance analysis, we obtain the theoretical expressions of the secrecy outage probability (SOP) for the non-colluding and the colluding eavesdroppers. And the closed form expressions of the asymptotic SOP in the high signal-to-noise ratio (SNR) region are derived to provide more insightful guidelines. The analytical results show that RSMA is able to meet different demands of covert performance, and the detection threshold depends on the number of RIS elements and the power distribution coefficient of RSMA to maximize the DEP. The numerical results illustrate that: 1) RIS-RSMA can realize a higher covert performance than RIS assisted transmissions; 2) a lower SOP can be achieved by deploying RIS with more elements.

With the popularization of 5G technology and the growth of data traffic demands, cellular traffic prediction has become crucial for network management. However, existing methods face the following challenges: (1) Most studies only consider normal traffic and neglect the dynamic impact of external environmental changes; (2) Due to model complexity, existing methods rely on short-term data and struggle to capture long-term traffic trends. To address these issues, this paper proposes a Multi-Feature Adaptive Spatiotemporal Trend Network (MASTTN), which includes feature embedding, mask reconstruction, and spatiotemporal trend extraction modules. First, a gated unit dynamically integrates temporal, spatial, and external features to capture the multidimensional correlations of traffic. Next, a random mask reconstruction approach extracts new sequences containing trend information from longer historical time series. Finally, dilated causal convolution and short-term spatiotemporal feature extraction modules are used to refine both long- and short-term features. Experimental results show that MASTTN achieves a minimum improvement of 5.63% and a maximum improvement of 16.78% in long-term prediction over 120 minutes, compared to baseline models, and the effectiveness of each module is verified through perturbation experiments.

The full-space capability of the reconfigurable intelligent surface (RIS) provides an intelligent radio environment for information transmissions. This paper investigates the RIS-assisted secure transmissions in the presence of multiple eavesdroppers (Eves), where the colluding and the non-colluding behaviors of Eves are considered. We first propose a tractable Gamma distribution to characterize the distributions of the signal-to-noise ratio (SNR) under the Rayleigh, the Rice and the Nakagami-m channels. Then we derive the theoretical expressions of the average security capacity (ASC) for colluding and non-colluding eavesdroppers. Besides, the asymptotic expressions of the ASC are also obtained when the SNRs are large in practical scenarios. The simulation results demonstrate that the RIS is capable of reducing the negative impact of the increase in the number of eavesdroppers on the security performance by deploying more RIS elements in both colluding and non-colluding eavesdroppers.

With the increasing density of base stations, the energy consumption of the 5-th generation mobile networks (5G) has become a serious issue and has attracted attention. To address this challenge, a novel model-based reinforcement learning (MBRL) algorithm for joint base station advanced sleep mode (ASM) and power control is proposed, aiming at optimizing the energy efficiency of small-cell networks. Taking the data rate and delay requirements of users into consideration, we develop a small-cell network model with diverse types of users. The energy efficiency optimization problem is formulated as a Markov decision process (MDP) and a model-based reinforcement learning approach is designed to solve this problem. Simulation shows that the proposed algorithm converges quickly and achieves higher energy efficiency (EE) and lower packet loss rates, compared with baseline methods, especially in high-load and high-interference conditions. This study provides both theoretical and practical insights for the green development of 5G and future communication networks.

The integrated communication and localization (I-CAL) has become as a pivotal technology in the evolution towards B5G and 6G networks, particularly for a variety of emerging wireless applications. In ICAL networks, resource allocation and beamforming design are critical components that significantly influence both the precision of localization and the efficiency of communication. Moreover, high accuracy synchronization is extremely challenging in wireless networks. In this paper, we investigate the trade-off between spectral efficiency (SE) and position error bound (PEB) by formulating a robust power and time-slot allocation and beamforming design problem for asynchronous ICAL networks with the imperfect initial position. We first illustrate the coupling between SE and position error through channel estimation error. Then, we derive a lower bound on the position error in terms of the Fisher information matrix (FIM). The alternating optimization, convex approximate and Bernstein-type inequality algorithms are proposed to solve the non-convex problems. Finally, the simulation results reveal the trade-off between SE and PEB, and validate the robustness and effectiveness of the proposed algorithms.

A user-centric no-cell (UCNC) architecture has been proposed to achieve a consistent user experience and enhance system capacity. However, high user mobility and complex network topologies present significant challenges in realizing these objectives. To tackle these challenges, we investigate the clustering and resource allocation problems in the UCNC network to maximize spectral efficiency (SE). We split this problem into two sub-problems: user-centric clustering and resource allocation. First, we propose a clustering algorithm based on mobility prediction and train a Long Short-Term Memory (LSTM) neural network to forecast user trajectories. Second, based on the clustering results, we develop an improved two-stage graph coloring algorithm to minimize interference between users and maximize resource block (RB) gains. Simulation results indicate that our proposed approach significantly outperforms the benchmark in terms of system SE while reducing signaling overhead produced by the updating of clusters.

Physical layer security is an important method to improve the secrecy performance of wireless communication systems. In this paper, we analyze the effect of employing channel correlation to improve security performance in multiple-input multiple-output (MIMO) scenario with antenna selection (AS) scheme. We first derive the analytical expressions of average secrecy capacity (ASC) and secrecy outage probability (SOP) by the first order Marcum Q function. Then, the asymptotic expressions of ASC and SOP in two specific scenarios are further derived. The correctness of analytical and asymptotic expressions is verified by Monte Carlo simulations. The conclusions suggest that the analytical expressions of ASC and SOP are related to the product of transmitting and receiving antennas; increasing the number of antennas is beneficial to ASC and SOP. Besides, when the target rate is set at a low level, strong channel correlation is bad for ASC, but is beneficial to SOP.

In this paper, a 4-weighted-type fractional Fourier transform (4-WFRFT) technology is presented to enhance the physical layer security (PLS) performance in a system of multiple eavesdroppers. The average signal-to-noise ratio (SNR) of eavesdroppers is first derived. Based on the derived average SNR, closed-form expressions of average security capacity (ASC) and secrecy outage probability (SOP) are obtained by employing the 4-WFRFT technology. The numerical results demonstrate that: 1) when the number of eavesdroppers increases, the 4 -WFRFT technology can also obtain a higher secrecy capacity. 2) the ASC decreases first but finally keeps at a constant value as the number of eavesdroppers increases. Simulations verify our analysis and demonstrate the 4 -WFRFT technology can validly improve the PLS performance in the multiple eavesdroppers system.

Unmanned aerial vehicles (UAVs) enable flexible data collection from Internet of Things (IoT) nodes in remote areas, but the data of IoT nodes face security threats. In the proposed data collection strategy based on a double cluster head (CH) framework, we exploit the inter-user interference (IUI) of uplink non-orthogonal multiple access (NOMA) to improve the security of IoT nodes. Specifically, inter-CH interference in NOMA is used as jamming signals to hide confidential data. Then a CH selection scheme is designed to alleviate the unbalanced energy consumption among member nodes in a cluster. Based on the CH selection scheme, we maximize the secrecy energy efficiency (SEE) via joint optimization of power, time scheduling, and trajectory. Due to the highly coupled variables and non-convex constraints, an alternating optimization method is used to decouple the original problem into subproblems and they are solved iteratively. In each iteration, Dinkelbach’s method is used to tackle the fractional objective function; the successive convex approximation technique is used to transform the non-convex subproblems into convex forms. In numerical simulations, our proposed data collection strategy shows effectiveness in improving SEE and hindering wiretapping. Furthermore, the proposed CH selection scheme efficiently extends the lifetime of energy-constrained IoT nodes.

Efficient information gathering is a critical task for the Internet of underwater things (IoUT) that empowers the marine industry, but energy-constrained devices have become a bottleneck in the sustainability of IoUT. In this article, we focus on the unmanned aerial vehicle (UAV)-aided marine data collection in an air-ocean integrated network. We consider the mobility of buoys caused by waves during a collection period to achieve robust transmission, instead of assuming stationary buoys. Our objective is to reduce the energy consumption of UAV's flight and the communication energy of buoys and sensors via joint trajectory planning and resource allocation while ensuring transmission robustness. Due to the nonconvex objective function and coupled variables in constraints, we use an alternating optimization method to divide the original problem into two subproblems and solve them iteratively. The successive convex approximation technique is used to tackle the nonconvex constraints. Furthermore, the wave-induced mobility of buoys results in infinite number of constraints. To tackle this issue, we use an S -procedure to transform these constraints into a series of deterministic linear matrix inequalities. Simulations reveal that the proposed scheme reduces energy consumption while achieving robust transmission. Specifically, the proposed scheme overcomes wave effects and achieves higher throughput than other nonrobust schemes. Meanwhile, our scheme is able to provide 27.3 % lower weighted energy consumption than the other robust transmission scheme.

The traditional information security based on cryptosystem is seriously threatened due to the exponential growth of computing capacity. In order to improve for the upper cryptosystem security, the secure transmission at the physical layer is introduced into the wireless communication system. However, considering the openness of wireless channel, the performance analysis of security wireless communication systems has become a hot issue in recent years. Due to the existence of the upper layer cryptosystem being cracked and the openness of wireless channel security problems, the Multiple-Input Multiple-Output (MIMO) technology increases the difference between channels, the development of its rich spatial degrees of freedom can greatly improve the channel capacity and achieve the purpose of enhancing the Physical layer security (PLS); On this basis, Weighted Type Fractional Fourier Transform (WFrFT) technology rotates and splits the constellation points of signals, it is equivalent to increasing the artificial noise to enhance the PLS. The Average security capacity is an important index to measure the PLS, Therefore, this paper deduces the Average security capacity in MIMO scenario and gives a specific closed expression when considering the channel correlation. By analyzing the expression, the increase of the number of MIMO antennas will improve the Average security capacity. When the estimated bias is 1 in 4-WFRFT, the Average security capacity will be significantly improved; the Average security capacity will reach the maximum value, thus enhancing the PLS.

Improving long-term user satisfaction in user-centric networks while ensuring an acceptable delay in the presence of an eavesdropper is an important yet challenging task. In this paper, we investigate a long-term secure resource allocation for a user-centric network. Our objective is to maximize the user's long-term average satisfaction by controlling the network-layer data arrival rate and physical-layer power allocation at access points. This objective is mainly constrained by average delay requirements, average leakage rate, and data-queue stability conditions. These constraints are challenging to directly solve because of less-analytical expressions. To tackle this issue, we introduce two virtual queues as the debt of delay and leakage data. By using a Lyapunov optimization approach, we incorporate the stabilities of these virtual queues and the real data backlog queue into the objective function as a penalty. This allows us to transform the long-term optimization problem into a sequence of short-term subproblems that can be analytically solved at each slot. Simulation reveals that the proposed long-term secure resource allocation scheme outperforms the snapshot-based method in terms of the user's long-term satisfaction.

Ultra-dense network (UDN) is an important technology to provide high data rate services in hot areas. Efficient resource allocation schemes are vital, in order to ensure the quality-of-service (QoS) requirements of users. In this paper, we consider the resource allocation problem in a UDN with both enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) users to find ways to improve energy efficiency (EE) and satisfy the diverse QoS requirements. We consider a more realistic power consumption model and interference model, in order to better satisfy the QoS requirements and mitigate the performance degradation caused by strong interference. Then, we construct an EE maximization problem with QoS constraints. In order to solve this non-convex combined integer fractional programming problem, we decompose it into two sub-problems: resource block (RB) allocation and power allocation problems. Algorithms based on continuous convex approximation and difference of convex programming are proposed to solve the sub-problems, respectively. Then we propose an alternating optimization algorithm to obtain a sub-optimal solution to the original problem and analyze the convergence and complexity of the algorithm. The simulation results show that the proposed algorithm can converge quickly and has obvious advantages in improving EE and guaranteeing the QoS requirements of eMBB and URLLC users.

The intensive deployment of sixth-generation (6G) base stations is expected to greatly enhance network service capabilities, offering significantly higher throughput and lower latency compared to previous generations. However, this advancement is accompanied by a notable increase in the number of network elements, leading to increased power consumption. This not only worsens carbon emissions but also significantly raises operational costs for network operators. To address the challenges arising from this surge in network energy consumption, there is a growing focus on innovative energy-saving technologies designed for 6G networks. These technologies involve strategies for dynamically adjusting the operational status of base stations, such as activating sleep modes during periods of low demand, to optimize energy use while maintaining network performance and efficiency. Furthermore, integrating artificial intelligence into the network’s operational framework is being explored to establish a more energy-efficient, sustainable, and cost-effective 6G network. In this paper, we propose a small base station sleeping control scheme in heterogeneous dense small cell networks based on federated reinforcement learning, which enables the small base stations to dynamically enter appropriate sleep modes, to reduce power consumption while ensuring users’ quality-of-service (QoS) requirements. In our scheme, double deep Q-learning is used to solve the complex non-convex base station sleeping control problem. To tackle the dynamic changes in QoS requirements caused by user mobility, small base stations share local models with the macro base station, which acts as the central control unit, via the X2 interface. The macro base station aggregates local models into a global model and then distributes the global model to each base station for the next round of training. By alternately performing model training, aggregation, and updating, each base station in the network can dynamically adapt to changes in QoS requirements brought about by user mobility. Simulations show that compared with methods based on distributed deep Q-learning, our proposed scheme effectively reduces the performance fluctuations caused by user handover and achieves lower network energy consumption while guaranteeing users’ QoS requirements.

In recent years, UAV techniques are developing very fast, and UAVs are becoming more and more popular in both civilian and military fields. An important application of UAVs is rescue and disaster relief. In post-earthquake evaluation scenes where it is difficult or dangerous for human to reach, UAVs and sensors can form a wireless sensor network and collect environmental information. In such application scenarios, task allocation algorithms are important for UAVs to collect data efficiently. This paper firstly proposes an improved immune multi-agent algorithm for the offline task allocation stage. The proposed algorithm provides higher accuracy and convergence performance by improving the optimization operation. Then, this paper proposes an improved adaptive discrete cuckoo algorithm for the online task reallocation stage. By introducing adaptive step size transformation and appropriate local optimization operator, the speed of convergence is accelerated, making it suitable for real-time online task reallocation. Simulation results have proved the effectiveness of the proposed task allocation algorithms.

Since the traffic administration at road intersections determines the capacity bottleneck of modern transportation systems, intelligent cooperative coordination for connected autonomous vehicles (CAVs) has shown to be an effective solution. In this paper, we try to formulate a Bi-Level CAVs intersection coordination framework, where coordinators from High and Low levels are tightly coupled. In the High-Level coordinator where vehicles from multiple roads are involved, we take various metrics including throughput, safety, fairness and comfort into consideration. Motivated by the time consuming space-time resource allocation framework, we try to give a low complexity solution by transforming the complicated original problem into a sequential linear programming one. Based on the “feasible tunnels” (FT) generated from the high-Level coordinator, we then propose a rapid gradient-based trajectory optimization strategy in the low-level planner, to effectively avoid collisions beyond high-level considerations, such as the unexpected pedestrian or bicycles. Simulation results and laboratory experiments show that our proposed method outperforms existing strategies. Moreover, the most impressive advantage is that the proposed strategy can plan vehicle trajectory in milliseconds, which is promising in real-world deployments. A detailed description include the coordination framework and experiment demo could be found at the supplement materials, or online at https://youtu.be/MuhjhKfNIOg.